FUP ALGORITHM TO DISCOVER WEIGHTED FREQUENT ITEMSETS FROM TRANSACTIONAL DATABASES

Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for hig h utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemset s. In this paper, we propose an algorithm for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets as per periodicity. The informat ion of high utility itemsets is maintained in a tree - based data structure named uti lity pattern tree such that candidate itemsets can be generated efficiently with only two scans of database. Experimental results show that the proposed algorithm not only reduce the number of candidates effectively but also outperform other algorithms sub stantially in terms of runtime and frequency based weights, especially when databases contain lots of very long transactions